llama-1B-5B

OLMoE-1B-7B-0125-Instruct — 30% Expert Pruned

This is an experimental version of allenai/OLMoE-1B-7B-0125-Instruct with approximately 30% of its routed experts physically removed.

The original model has 64 experts in each of its 16 mixture-of-experts layers. This version removes 19 experts independently from every layer, leaving 45 experts per layer. The original top-8 routing is preserved.

The purpose of this release is to explore a simple question:

How much of a mixture-of-experts model can be removed when experts are ranked by usage, contribution, and functional redundancy rather than parameter magnitude alone?

This is a research artifact. Removing nearly one-third of the expert pool is an aggressive intervention and may remove capabilities that appear only in particular domains or prompt distributions.

In testing against the unpruned base model, this 30%-cut version showed an approximately 6% average performance decrease across the same MMLU, ARC-Challenge, HellaSwag, and GSM8K benchmark screen used during the pruning experiments. This provides an initial measure of the quality tradeoff: about 30% of the expert pool was removed for an observed average benchmark loss of about 6%.

What was removed

Nineteen experts were removed from each layer. Expert roles are layer-specific, so candidates were selected independently in every layer rather than removing the same expert indices throughout the network.

For each removed expert, both the expert MLP and its corresponding router row were deleted. The router therefore produces 45 logits instead of 64, and its softmax renormalizes probability over the experts that remain.

Attention layers, embeddings, normalization layers, and retained expert tensors are not reduced by the 30% expert cut. This is why the final file is smaller by slightly less than 30%.

The pruning idea

Mixture-of-experts models contain a large pool of expert MLPs, but only a small subset is selected for each token. Some experts are used frequently across many prompts, while others are activated rarely or behave similarly to other experts in the same layer.

The pruning hypothesis is that part of this expert pool is redundant. If the least important experts can be identified, their parameters and router entries can be removed without changing the rest of the network.

The difficult part is deciding what "least important" means. Routing frequency alone is not sufficient: an expert may be rarely used globally while remaining important for code, mathematics, multilingual text, or structured output.

How experts were ranked

The pruning process used multiple signals for every expert in every layer:

The signals were combined into a layer-local importance score. Low usage was therefore not enough by itself to make an expert a removal candidate. An expert also needed to have low measured contribution or substantial functional overlap with experts that would remain.

Calibration examples covered general knowledge, science, commonsense, mathematics, code, multilingual text, and structured formats. Including several domains reduces the risk of classifying a narrow specialist as unused only because it is quiet on ordinary English prompts.

The process for each MoE layer was:

1. Collect routing and expert-output statistics on the calibration mix.
2. Compare expert behavior on shared hidden states.
3. Protect strong contributors observed in each calibration domain.
4. Rank the remaining candidates by multi-signal importance.
5. Remove the lowest-ranked 19 experts and their router rows.
6. Preserve the original top-8 routing over the 45 retained experts.

Deletion rather than weight averaging

This model uses structural expert deletion. Removed experts are not replaced with averaged MLP weights.

Two expert MLPs may produce similar outputs while using different internal hidden-unit permutations. Directly averaging their parameters can therefore damage both functions. Functional similarity is used as evidence of redundancy, not as justification for naive parameter averaging.

Because the model is physically compacted, the removed expert tensors no longer occupy storage or resident memory. This differs from router masking, where all expert parameters remain in the checkpoint even if some experts can never be selected.

What the 30% cut changes

The expert pool is reduced from 64 to 45 choices in every layer. Each token is still routed to 8 experts, so a larger fraction of the remaining pool is active for each token:

• Original: 8 of 64 experts, or 12.5% of the layer's expert pool.
• Pruned: 8 of 45 experts, or 17.8% of the layer's expert pool.

This release primarily targets model size and resident memory. It does not promise a proportional reduction in inference FLOPs because the number of active experts per token remains unchanged.

Pruning can also change routing decisions even when every retained tensor is copied exactly. Removing router outputs changes the softmax distribution and may change which experts enter the top-8 selection, particularly when router scores are close.

Released artifact

The local upstream Q4_K_M conversion is 3.92 GiB. The 30%-pruned Q4_K_M file is approximately 27.54% smaller. The remaining difference from 30% comes from the model parameters that are not part of the expert MLP pool.

The GGUF metadata reports 16 OLMoE blocks, 45 experts per block, 8 active experts per token, and a 4,096-token context length.

Running with llama.cpp

llama-cli \
  -m OLMoE-1B-7B-0125-Instruct-30pct-Q4_K_M.gguf \
  -cnv -ngl 99 -c 4096

To run an OpenAI-compatible local server:

llama-server \
  -m OLMoE-1B-7B-0125-Instruct-30pct-Q4_K_M.gguf \
  -ngl 99 -c 4096 --port 8080

Reduce -ngl if the model does not fit in available GPU memory. The artifact contains the upstream chat template in its GGUF tokenizer metadata.

Testing status

This exact 30%-pruned Q4_K_M artifact was successfully loaded and used for generation with llama.cpp on an NVIDIA RTX 3090.

The pruned model was also compared with the unpruned base model using the same task benchmark suite used elsewhere in the pruning experiment:

• MMLU;
• ARC-Challenge;
• HellaSwag; and
• GSM8K.

Across that suite, the 30%-cut model had an approximately 6% average drop relative to the base model. This is an aggregate summary, not a claim that every individual task decreased by exactly 6%.

The detailed result file is no longer available, so per-task scores, sample counts, and statistical uncertainty cannot be reproduced from this release. The aggregate result is included for transparency but should be treated as a small benchmark screen rather than a publication-grade evaluation.

Limitations

• Removing 19 of 64 experts per layer is aggressive and may reduce quality.
• Calibration can miss specialists outside its sampled domains.
• Rare-language, code, structured-output, or other narrow capabilities may be affected even when general chat behavior appears reasonable.
• Top-8 routing remains unchanged, so active-compute savings are modest.
• Q4_K_M quantization introduces an additional source of quality loss beyond expert pruning.
• The retained aggregate benchmark result shows an approximately 6% average decrease, but the deleted detailed results prevent per-task analysis or independent reproduction of that figure from this repository.
• The model inherits the upstream model's biases and safety limitations.

This model should be evaluated on representative data before practical use. It is best treated as an experiment in structural MoE compression rather than a drop-in replacement for the upstream checkpoint.

Upstream model and license

This work is derived from allenai/OLMoE-1B-7B-0125-Instruct and retains its Apache 2.0 license. See the upstream model card for training data, post-training, safety information, and citations. Architecture details are available in the OLMoE paper.

Conclusion

Removing approximately 30% of the model's expert pool while observing only an approximately 6% average drop across the tested benchmarks suggests that a meaningful amount of expert capacity may be redundant for the evaluated task distribution. Although the benchmark screen is too limited to establish broad quality parity, the difference between the scale of the structural reduction and the measured performance loss warrants further research into MoE pruning.

More broadly, these results support research into less wasteful MoE designs: models that preserve useful specialization while requiring fewer stored parameters, less resident memory, and less underused expert capacity. Larger and more diverse evaluations, improved specialist detection, iterative pruning, and post-pruning recovery or distillation could help determine how much redundancy can be removed without disproportionately harming rare-domain capabilities.